Thursday, May 31, 2012

Detour Crowd Path Replanning

Path replanning for Detour Crowd has been a long time pet peeve of mine. I've blogged about the problems surrounding it earlier, but very little code has been submitted. I've been testing some ideas along the way, but never managed to have the few hours of solid coding to get all the pieces together.

Here's what happens in the above video:

At start all the agents find a short parts towards the goal. In the video they intentionally get stuck at local minima at start. Generally this improves responsiveness, but sometimes the agents move to wrong direction for a moment.

During movement the agents will replan their paths if they notice that the navmesh has changed in front of them. When the agents turn to purple, it means that they are replanning their paths. Similar small search is performend when the replanning occurs, but polygons along the existing path are favored. This makes changes close the agent responsive and changes further away does not make any visual change at all.

When the agent approaches the end of the path and the last polygon on the path is not the goal polygon, the agent will periodically replan the path. When a pathway to the goal opens, the agent will use that path.

On top of that there is the path topology optimization going on too. I think the recent changes made that pretty much irrelevant–I think it might be better to just fire a replan every now and then just for the kicks.

I think that summarizes my thinking about path following in general. Once the world is constantly changing, the system ends up replanning the whole time. You cannot replan as much as you would like to since it is hard to determine how long A* will take, so the system becomes this weird scheduler-caching-magic-fudge-latency-hiding-box. There are these dynamic planning algos, like D*, but they all require too much memory.

I hope to get to have the change to put another pass on Detour Crowd replanner to make it much simpler and more robust. Now there is too much state management spaghetti in there.

The changes are available in SVN, please not that I removed the adjust move target function as it was complicating the logic too much. If you are using it, please let me know and I'll try to figure out similar functionality. I also added velocity control for agents (for player characters and such).

10 comments:

Might be worth seeing if the initial short path is obviously blocked (raycastishly) so it won't look *too* stupid.

Under what conditions do agents reach the end of the path but it's not the goal polygon? I couldn't tell if the agents had found some alternate route around or if they were just gathering under the goal as the nearest place they could find.

Assuming the latter, and you're not computing incomplete paths, then I assume this will suffer from agents never noticing that a better path opened up somewhere behind/sideways to them? So some kind of hierarchical pathing with incomplete paths and then replanning at the end of the incomplete step might be better (although I guess it's an open question how realistic it is for agents to discover better paths when their current path is valid).

A crazy idea I thought of while watching the video: for groups of agents with the same destination, you could actually try to do something where each one replans less often, but then if they notice a same-goal-agent going the opposite direction, they replan sooner, although more realistically this would happen not just with opposite direction--movement.

My intuition says that the raycastish metric won't be very good. But on a second though maybe the open node that has best score could be better than just best score. I think heuristics like these will always fail in one way or the other. For example that movement you see in the video would be totally cool for "investigate" behavior but looks stupid if the intent is to reach the goal in a rush.

If the path finder cannot reach the end polygon, then it will pick the polygon that has highest score. That's the reason the guys find path under the platform. Also, once the agents reach the end of incomplete path, they will replan every few seconds (that's why it looks like christmas under the platform), if new opening is found, they will continue to follow it.

My idea is to provide callback function in the API so that the caller can decide how many times and how often impartial paths are replanned, and whether it is ok to just stop at the end of an incomplete path. This is where the path following gets really game and behavior specific. There is no correct solution, as sometimes the right answer is to wait 15 minutes.

I find it always funny that one of the most important actions of path planning and moving is staying still :)

Group path planning definitely should use one "ghost agent" which does the full path planning and other agents would just do small searches to keep up with the ghost.

Does the localized planning more or less correspond to the agent's field of view? That could help solve the problem that an agent appears to be omniscient about whether a path is blocked in the distance or not.

I am a big fan of regularly scheduled path re-planning. I agree with your comments about it. To me, this sort of approach means accepting that the world is dynamic, and you know it's going to change, so why not continually re-plan. I think re-planning solves a lot of problems with a minimal amount of code. There are certainly other solutions to solving some of these problems, but I tend to like the re-planning model better, for whatever reason.

When I created SimplePath for Unity, I included a re-plan interval that you can specify, to determine how frequently paths are re-planned (ex: every 0.5 seconds)

I think it is also useful for correcting certain bugs that occur due to the dynamic nature of the game environment. For example, physics are sometimes battling against the navigation system. If the agent gets pushed off the nav mesh because of physics, he needs to discover that he is out of bounds, and plan his way back in bounds. With frequent re-planning, you don't have to worry about sending specific events for situations like this, because the agent should detect that he is out of bounds during his next planning phase, and plan a proper path back in bounds.

I know SimplePath is not nearly as developed as your software, but i do think that handling dynamic situations was one of its strengths. Of course, that is easier to do when you are on a grid :]

If you take a look at the yellow paths in the dynamic portion of the video, I think there are a few things of note. You can clearly tell that the agent is replanning, because he is avoiding the obstacles, and because the yellow line will sometimes jitter a little bit, back and forth between two or so squares. Initially I thought that this would have been a problem. But the visual result of the agent looks fine; the line jitters sometimes as a result of this replanning, but the agent does not. I didn't write any sort of special case weighing for preferring some recently cached path over another, it's pretty much just straight forward A*.

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